<?php

namespace PHPExcel\Shared\Trend;

/**
 * PHPExcel_Exponential_Best_Fit
 *
 * Copyright (c) 2006 - 2015 PHPExcel
 *
 * This library is free software; you can redistribute it and/or
 * modify it under the terms of the GNU Lesser General Public
 * License as published by the Free Software Foundation; either
 * version 2.1 of the License, or (at your option) any later version.
 *
 * This library is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
 * Lesser General Public License for more details.
 *
 * You should have received a copy of the GNU Lesser General Public
 * License along with this library; if not, write to the Free Software
 * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
 *
 * @category PHPExcel
 * @package PHPExcel_Shared_Trend
 * @copyright Copyright (c) 2006 - 2015 PHPExcel (http://www.codeplex.com/PHPExcel)
 * @license http://www.gnu.org/licenses/old-licenses/lgpl-2.1.txt LGPL
 * @version ##VERSION##, ##DATE##
 */
class ExponentialBestFit extends BestFit {
    /**
     * Algorithm type to use for best-fit
     * (Name of this Trend class)
     *
     * @var string
     *
     */
    protected $bestFitType = 'exponential';
    
    /**
     * Return the Y-Value for a specified value of X
     *
     * @param float $xValue
     *            X-Value
     * @return float Y-Value
     *        
     */
    public function getValueOfYForX($xValue) {
        return $this->getIntersect() * pow($this->getSlope(), ($xValue - $this->xOffset));
    }
    
    /**
     * Return the X-Value for a specified value of Y
     *
     * @param float $yValue
     *            Y-Value
     * @return float X-Value
     *        
     */
    public function getValueOfXForY($yValue) {
        return log(($yValue + $this->yOffset) / $this->getIntersect()) / log($this->getSlope());
    }
    
    /**
     * Return the Equation of the best-fit line
     *
     * @param int $dp
     *            Number of places of decimal precision to display
     * @return string
     *
     */
    public function getEquation($dp = 0) {
        $slope = $this->getSlope($dp);
        $intersect = $this->getIntersect($dp);
        
        return 'Y = ' . $intersect . ' * ' . $slope . '^X';
    }
    
    /**
     * Return the Slope of the line
     *
     * @param int $dp
     *            Number of places of decimal precision to display
     * @return string
     *
     */
    public function getSlope($dp = 0) {
        if ($dp != 0) {
            return round(exp($this->_slope), $dp);
        }
        return exp($this->_slope);
    }
    
    /**
     * Return the Value of X where it intersects Y = 0
     *
     * @param int $dp
     *            Number of places of decimal precision to display
     * @return string
     *
     */
    public function getIntersect($dp = 0) {
        if ($dp != 0) {
            return round(exp($this->intersect), $dp);
        }
        return exp($this->intersect);
    }
    
    /**
     * Execute the regression and calculate the goodness of fit for a set of X and Y data values
     *
     * @param float[] $yValues
     *            The set of Y-values for this regression
     * @param float[] $xValues
     *            The set of X-values for this regression
     * @param boolean $const            
     */
    private function exponentialRegression($yValues, $xValues, $const) {
        foreach ( $yValues as &$value ) {
            if ($value < 0.0) {
                $value = 0 - log(abs($value));
            } elseif ($value > 0.0) {
                $value = log($value);
            }
        }
        unset($value);
        
        $this->leastSquareFit($yValues, $xValues, $const);
    }
    
    /**
     * Define the regression and calculate the goodness of fit for a set of X and Y data values
     *
     * @param float[] $yValues
     *            The set of Y-values for this regression
     * @param float[] $xValues
     *            The set of X-values for this regression
     * @param boolean $const            
     */
    public function __construct($yValues, $xValues = array(), $const = true) {
        if (parent::__construct($yValues, $xValues) !== false) {
            $this->exponentialRegression($yValues, $xValues, $const);
        }
    }
}
